Computational Complexity A Modern Approach Solution Manual
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Computational Complexity
Author: Sanjeev Arora
language: en
Publisher: Cambridge University Press
Release Date: 2009-04-20
This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set. The book starts with a broad introduction to the field and progresses to advanced results. Contents include: definition of Turing machines and basic time and space complexity classes, probabilistic algorithms, interactive proofs, cryptography, quantum computation, lower bounds for concrete computational models (decision trees, communication complexity, constant depth, algebraic and monotone circuits, proof complexity), average-case complexity and hardness amplification, derandomization and pseudorandom constructions, and the PCP theorem.
Understanding Machine Learning
Author: Shai Shalev-Shwartz
language: en
Publisher: Cambridge University Press
Release Date: 2014-05-19
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Linear Algebra with Maple, Lab Manual
Linear Algebra: An Introduction Using MAPLE is a text for a first undergraduate course in linear algebra. All students majoring in mathematics, computer science, engineering, physics, chemistry, economics, statistics, actuarial mathematics and other such fields of study will benefit from this text. The presentation is matrix-based and covers the standard topics for a first course recommended by the Linear Algebra Curriculum Study Group. The aim of the book is to make linear algebra accessible to all college majors through a focused presentation of the material, enriched by interactive learning and teaching with MAPLE. Development of analytical and computational skills is emphasized throughout Worked examples provide step-by-step methods for solving basic problems using Maple The subject's rich pertinence to problem solving across disciplines is illustrated with applications in engineering, the natural sciences, computer animation, and statistics